Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

“…Four machine learning algorithms, known as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and naive Bayes (NB), were used to develop models for predicting the risk of lower extremity DVT occurrence in GC patients. …”
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Using dynamic Bayesian belief networks to infer the effects of climate change and human activities on changes in regional ecosystem services by Haoran Yu, Junyi Jiang, Xinchen Gu, Chunwu Cao, Cheng Shen

“…Moreover, land use and land cover, as reflections of human activities, greatly affect Carbon storage (CF) through the expansion of construction land and the loss of forested areas. Soil retention (SR), on the other hand, is predominantly influenced by rainfall. …”
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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway by Mohib Ullah, Haijun Qiu, Wenchao Huangfu, Dongdong Yang, Yingdong Wei, Bingzhe Tang

“…To address this, this study assessed the performance of six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF and CNN+CatBoost), and a Stacking Ensemble (SE) combining CNN, RF, and CatBoost in mapping landslide susceptibility along the Karakoram Highway in northern Pakistan. …”
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Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability by Muhammad Paend Bakht, Mohd Norzali Haji Mohd, Babul Salam KSM Kader Ibrahim, Nuzhat Khan, Usman Ullah Sheikh, Ab Al-Hadi Ab Rahman

“…The top four performing models, achieving the highest predictive accuracies, were identified as Extra Tree (91% accuracy), Random Forest (85%), XGBoost (75%), and Decision Tree (68%) for further analysis. …”
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Automated differentiation of wide QRS complex tachycardia using QRS complex polarity by Adam M. May, Bhavesh B. Katbamna, Preet A. Shaikh, Sarah LoCoco, Elena Deych, Ruiwen Zhou, Lei Liu, Krasimira M. Mikhova, Rugheed Ghadban, Phillip S. Cuculich, Daniel H. Cooper, Thomas M. Maddox, Peter A. Noseworthy, Anthony Kashou

“…Methods In a three-part study, we derive and validate machine learning (ML) models—logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)—using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. …”
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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditi... by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

“…Also, the MR was predicted by the MC, and the drying rate (DR), drying times, and final thickness were predicted using the multi-layer perceptron (MLP), gaussian process (GP), k-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR) algorithms. …”
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Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study by Adriana Ivanescu, Simona Popescu, Adina Braha, Bogdan Timar, Teodora Sorescu, Sandra Lazar, Romulus Timar, Laura Gaita

“…With the use of machine learning, the patients were assessed and categorized as having one of the three main types of cataracts: cortical (CC), nuclear (NS), and posterior subcapsular (PSC). A Random Forest Classification algorithm was employed to predict the incidence of different associations of cataracts (1, 2, or 3 types). …”
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Establishment and Validation of a Machine‐Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra‐Abdominal Candidiasis in Septic Patients by Jiahui Zhang, Wei Cheng, Dongkai Li, Guoyu Zhao, Xianli Lei, Na Cui

“…A machine‐learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. …”
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Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer by Lin Ni, Lin Ni, He Li, Yanqi Cui, Wanqiu Xiong, Shuming Chen, Hancong Huang, Zhiwei Wang, Hu Zhao, Hu Zhao, Hu Zhao, Bing Wang, Bing Wang, Bing Wang

“…The above 62 DEGs were included in Cox analysis, LASSO regression, Random Forest and SVMV-RFE, respectively, and then the intersection was used to obtain 5 prognostic related characteristic genes (SUV39H2, OPN4, RORB, FBXL6 and SIAH2). …”
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Assessing the Direct Impact of Typhoons on Vegetation Canopy Structure and Photosynthesis by Yaoyao Zheng, Simin Zhan, Zaichun Zhu, Sen Cao, Jiana Chen, Pengjun Zhao, Weimin Wang, Ranga B. Myneni

“…This study proposes a novel framework for quantifying typhoons’ immediate and long-term impacts on vegetation canopy structure and photosynthesis. We developed random forest models based on satellite-observed leaf area index (LAI) and environmental data during typhoon-free periods to simulate LAI under non-typhoon conditions. …”
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Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods by Rui Chen, Zean Liu, Jingyan Yang, Tiantian Ma, Aihong Guo, Rongguang Shi

“…The results show that the best accuracy of the random forest probability model in the rice dataset is 0.89. …”
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Digital mapping of soil organic carbon in a plain area based on time-series features by Kun Yan, Decai Wang, Yongkang Feng, Siyu Hou, Yamei Zhang, Huimin Yang

“…SOC prediction models were established using random forests (RF), backpropagation neural networks (BP), and support vector machines (SVM). …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

“…Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. …”
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Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China by Tiantian Tang, Yifan Wu, Yujie Li, Lexi Xu, Xinyi Shi, Haitao Zhao, Guan Gui

“…We developed and compared five deterministic precipitation forecasting models, including elastic net regression (ENR), support vector regression, random forest, extreme gradient boosting, and light gradient boosting to provide forecasts with lead times ranging from 0 to 5 months at a spatial resolution of 0.5<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>. …”
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Climate drives the long-term ant male production in a tropical community by Adriana Uquillas, Nathaly Bonilla, Stephany Arizala, Yves Basset, Héctor Barrios, David A. Donoso

“…From 2002 to 2017, we recorded male ant incidence of 155 ant species at ten malaise traps on the 50-ha ForestGEO plot in Barro Colorado Island. In this Panamanian tropical rainforest, traps were deployed for two weeks during the wet and dry seasons. …”
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Enhancing Tropical Cyclone Risk Assessments: A Multi-Hazard Approach for Queensland, Australia and Viti Levu, Fiji by Jane Nguyen, Michael Kaspi, Kade Berman, Cameron Do, Andrew B. Watkins, Yuriy Kuleshov

“…This study develops an integrated methodology for TC multi-hazard risk assessment that utilises the following individual assessments of key TC risk components: a variable enhanced bathtub model (VeBTM) for storm surge-driven hazards, a random forest (RF) machine learning model for rainfall-induced flooding, and indicator-based indices for exposure and vulnerability assessments. …”
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Metabarcoding the night sky: Monitoring landscape-scale insect diversity through bat diet by Cynthia Tobisch, Svenja Dege, Bernd Panassiti, Julian Treffler, Christoph Moning

“…Species composition in the diet showed high variation in space and time, but was also associated with edge density and the proportion of grassland within 2 km radius of the roosts. Moreover, forest and grassland percentages within 2-km buffers around the roosts significantly increased species richness within the diet. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). …”
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Easy, fast and reproducible Stochastic Cellular Automata with chouca by Génin, Alexandre, Dupont, Guillaume, Valencia, Daniel, Zucconi, Mauro, Ávila-Thieme, M. Isidora, Navarrete, Sergio A., Wieters, Evie A.

“…They are widely used to understand how small-scale processes scale up to affect ecological dynamics at larger spatial scales, and have been applied to a wide diversity of theoretical and applied problems in all systems, such as arid ecosystems, coral reefs, forests, bacteria, or urban growth. Despite their wide applications, SCA implementations are often ad-hoc, lacking performance, guarantees of correctness and poorly reproducible. …”
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A Study on the Infrageneric Classification of <i>Hordeum</i> Using Multiple Methods: Based on Morphological Data by Nayoung Ro, Pilmo Sung, Mesfin Haile, Hyemyeong Yoon, Dong-Su Yu, Ho-Cheol Ko, Gyu-Taek Cho, Hee-Jong Woo, Nam-Jin Chung

“…This study addresses these limitations by employing an integrative approach combining hierarchical clustering, Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), and Random Forest (RF) to analyze the compiled morphological datasets. …”
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